{"url":"/dataset/qure","name":"QuRe","full_name":null,"description_markdown":"### Introduction\r\nGeneralized quantifiers (e.g., few, most) are used to indicate the proportions predicates are satisfied. QuRe is quantifier reasoning dataset from [Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models](https://arxiv.org/pdf/2311.04659). It includes real-world sentences from Wikipedia and human annotations of generalized quantifiers from English speakers. \r\n\r\n### Sample\r\n```\r\n{\r\n    \"orig_sentence\": \"In order for a steel to be considered stainless it must have a Chromium content of at least 10.5%.\", \r\n    \"percentage\": \"10.50%\", \r\n    \"percentage_index\": 0, \r\n    \"math_expr\": \">=0.105\", \r\n    \"quant_sent\": \"In order for a steel to be considered stainless it must have some Chromium content.\", \r\n    \"quantifier\": \"some\", \r\n    \"quantifier_position\": 12, \r\n    \"specificity\": \"unable\", \r\n    \"wiki_entity\": \"List of blade materials\", \r\n    \"topics\": \"metallurgy; steel; composition\"\r\n}\r\n```\r\n   * orig_sentence: the original sentence appeared in Wikipedia.\r\n   * percentage: the percentage mentioned in the orig_sentence.\r\n   * percentage_index: the index of the mentioned percentage in the orig_sentence.\r\n   * math_expr: the percentage expression generated.\r\n   * quant_sent: the annotated quantified sentence.\r\n   * quantifier_position: the position of quantifier mentioned.\r\n   * specificity:  the difficulty of deciphering the percentage scope of the quantifier from the sentence excluding the quantifier.\r\n   * wiki_entity: the wikipedia entity that includes <i>orig_sentence</i> in the wikipage content.\r\n   * topics: sentence topics.\r\n\r\n### Load Dataset\r\n```\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"billli/QuRe\")\r\n```\r\n     \r\n### Reference\r\n```\r\n@inproceedings{li-etal-2023-pragmatic,\r\n    title = \"Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models\",\r\n    author = \"Li, Yiyuan  and\r\n      Menon, Rakesh  and\r\n      Ghosh, Sayan  and\r\n      Srivastava, Shashank\",\r\n    editor = \"Bouamor, Houda  and\r\n      Pino, Juan  and\r\n      Bali, Kalika\",\r\n    booktitle = \"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing\",\r\n    month = dec,\r\n    year = \"2023\",\r\n    address = \"Singapore\",\r\n    publisher = \"Association for Computational Linguistics\",\r\n    url = \"https://aclanthology.org/2023.emnlp-main.38\",\r\n    pages = \"573--591\",\r\n}\r\n```","description_withheld":null,"homepage":"https://huggingface.co/datasets/billli/QuRe","introduced_date":"2023-11-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/pragmatic-reasoning-unlocks-quantifier","title":"Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models","first_author":"Yiyuan Li","url":null},"license":{"name":"Apache-2.0","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Common Sense Reasoning","url":"/task/common-sense-reasoning","datasets_with_task":"/datasets/task/common-sense-reasoning"},{"name":"Logical Reasoning","url":"/task/logical-reasoning","datasets_with_task":"/datasets/task/logical-reasoning"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["QuRe"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}